April, 2013

The Microsoft Research Connections blog shares stories of collaborations with computer scientists at academic and scientific institutions to advance technical innovations in computing, as well as related events, scholarships, and fellowships.

Standing on the banks of the Seine, I found myself marveling at the beauty of “April in Paris” (cue the 1930s song). Perhaps it was the scent of the flowers in bloom in the Jardin du Luxembourg or the warmth of the Continental spring. But, most likely, my contentment stemmed from the success of the just-completed Microsoft Research Machine Learning Summit, which took place April 22 to 24 in the “City of Light” on Le Campus de Microsoft France.

The event brought together more than 230 attendees and presenters, including thought leaders from computer science, engineering, statistics, and mathematics. Through keynotes, demos, and panel discussions, we highlighted some of the key challenges in this new era of machine learning and explored the next generation of approaches, techniques, and tools that researchers and scientists need to exploit the information revolution for the benefit of society.

As exciting as the in-person event was, I was equally enthused by the reception of our streaming broadcast of key presentations and interviews, which was viewed by some 3,000 people around the globe. The live, online presentation not only made it possible for many more people to view the summit, it gave a broader group of students and researchers an opportunity to engage directly with some of the top experts in the field of machine learning, including Andrew Blake, director of Microsoft Research Cambridge, and Judea Pearl, professor emeritus at UCLA. I was pleased that many from the online audience posed questions about computer vision to Professor Blake, and difficult questions about probability and causality to Professor Pearl.

There was hardly an area of machine learning that wasn’t explored in depth at the summit—from the aforementioned topics of computer vision and causality, to insightful presentations on Bayesian statistics and the use of machine learning techniques in the realm of social media and large-scale learning.

Of course the food was outstanding (it was Paris, after all), and meals were made all the more enjoyable by the stimulating conversation of our companions and the spectacular views of Paris from the thirty-fourth floor of our hotel. But for me, the most exciting moments were the intense discussions I observed taking place during breaks and the social events, and the sense that seeds of exciting new ideas were being planted that would germinate in the months and years ahead.

In the late 1800s, the guanaco, a close relative of the llama, was hunted to near extinction. As we mark this year’s Earth Day (April 22), I want to share my excitement about a new tool that looks to make the future a little brighter for the guanaco and other threatened species in Latin America. That new tool is LiveANDES (Advanced Network for the Distribution of Endangered Species).

Developed by a partnership among researchers at the Pontifical Catholic University of Chile, the LACCIR (Latin American and Caribbean Collaborative ICT Research) Virtual Institute, and Microsoft Research, LiveANDES is designed to collect, house, and analyze data about Latin America’s wildlife—data that could prove vital to the preservation of the region’s rich but increasingly threatened biodiversity, which has suffered grievously from loss of habitat and climate change.

Mariano de la Maza, a wildlife officer in Chile’s Parks and Protected Areas Service, sees this decline on a daily basis. “The main problems of the Chilean forest are habitat loss and the fragmentation and degradation of native forests,” he says.

LiveANDES begins with field observations, made not just by wildlife biologists and park rangers but by “citizen scientists,” including hikers, eco-tourists, and other nature enthusiasts. As Cristian Bonacic, director of the wildlife laboratory at Pontifical Catholic University, notes, “When people go to the wild, they can encounter an endangered animal by chance.” These chance encounters can provide extremely valuable information about the location and status of threatened and endangered wildlife.

All that’s needed is a smartphone equipped with the LiveANDES app. Imagine you’re hiking in the Chilean countryside, and you think you’ve spotted a rare species. You simply take its picture with your smartphone and upload the picture and any sighting comments into LiveANDES. Your photo and annotations, along with the phone’s recognition of your geographical location and a time stamp, are then logged into the LiveANDES database, ready for parsing by the university team.

Once processed, the data becomes available to scientists locally and around the world, as well as to the public, in both Spanish and English. Bonacic praises LiveANDES for the way it helps researchers “share that information with the scientific community, park rangers, and people at large.”

Knowing where and under what circumstances a threatened species is living can help biologists devise strategies to stabilize and, one hopes, restore these vulnerable populations. Moreover, the information gathered in LiveANDES also will help keep the International Union for Conservation of Nature (IUCN) red list of endangered and threatened species accurate, complete, and up-to-date.

The LiveANDES platform was built by using Microsoft technologies, including Windows Phone, Microsoft SQL Server data management software, and Bing Maps for locating and visualizing the animals, and the Microsoft .NET Framework for programming. It not only houses data about Latin America’s wildlife, including photographs, audio and video recordings, and location and sighting data, but it also makes parsing huge volumes of data manageable for researchers.

According to Ignacio Casas, the executive director of LACCIR, LiveANDES integrates with the fourth paradigm, a foundational concept of eScience, in which data-intensive computing facilitates scientific discovery. LiveANDES is designed to make parsing the huge volumes of data recorded manageable for researchers.

Bonacic and his colleagues look forward to receiving a barrage of wildlife data from rangers, biologists, and, of course, citizen scientists. Thanks to LiveANDES, this data deluge will be manageable and actionable.

I am inspired by this project, as it tackles an extremely challenging environmental problem, which is the rapid decline of important elements of our natural heritage. Each animal species is an important piece of a puzzle, and each citizen scientist and researcher can play a crucial role in the preservation of endangered species for the next generation. I’m hopeful that LiveANDES will help the guanaco and other vulnerable species survive to see Earth Day 2113!

This April, Paris will be even more exciting than usual, as the Microsoft Research Machine Learning Summit takes place on the company’s “Le Campus.” This year, we will be streaming the keynotes and interviews live from the summit on April 23, from 13:30 to 17:00 GMT (9:30 A.M. to 1:00 P.M. Eastern Time and 6:30 A.M. to 10:00 A.M. Pacific Time).

This free online event will kick off at 13:30 GMT with the opening keynote (recorded earlier in the day) from Andrew Blake, director of Microsoft Research Cambridge. Professor Blake will describe advances in computer vision, with machines that learn to see. Then at 15:00 GMT, you can watch the live stream of Judea Pearl, director of the Cognitive Systems Laboratory at the University of California, Los Angeles. Professor Pearl will speak about the development and application of mathematical tools to study cause-and-effect relationships. What’s more, following their keynotes, these renowned experts will conduct an online Q&A—giving you the opportunity to engage directly with these eminent researchers.

In addition, there will be “Research in Focus” interview segments that describe cutting-edge work in machine learning. Fei-Fei Li of the Stanford Vision Lab and Sebastian Nowozin of Microsoft Research will discuss developments in teaching machines to see, and Zoubin Ghahramani of the University of Cambridge will describe his work on building an “automated statistician.”